Influence maximization across heterogeneous interconnected networks based on deep learning

Abstract With the fast development of online social networks, a large number of their members are involved in more than one social network. Finding most influential users is one of the interesting social network analysis tasks. The influence maximization (IM) problem aims to select a minimum set of users who maximize the influence spread on the underlying network. Most of the previous researches only focus on a single social networks, whereas in real world, users join to multiple social networks. Thus, influence can spread through common users on multiple networks. Besides, the existing works including simulation based, proxy based and sketch based approaches suffer from different issues including scalability, efficiency and feasibility due to the nature of these approaches for exploring networks and computation of their influence diffusion. Moreover, in the previous algorithms, several heuristics are employed to capture network topology for IM. But, these methods have information loss during network exploration because of their pruning strategies. In this paper, a new research direction is presented for studying IM problem on interconnected networks. The proposed approach employs deep learning techniques to learn the feature vectors of network nodes while preserving both local and global structural information. To the best of our knowledge, network embedding has not yet been used to solve IM problem. Indeed, our algorithm leverages deep learning techniques for feature engineering to extract all the appropriate information related to IM problem for single and interconnected networks. Moreover, we prove that the proposed algorithm is monotone and submodular, thus, an optimal solution is guaranteed by the proposed approach. The experimental results on two interconnected networks including DBLP and Twitter-Foursquare illustrate the efficiency of the proposed algorithm in comparison to state of the art IM algorithms. We also conduct some experiments on NetHept dataset to evaluate the performance of the proposed approach on single networks.